Functional Area:

FA - FinanceEstimated Travel Percentage (%): Up to 25%Relocation Provided: YesAIG Europe (Services) Limited

Position Summary

Science team at AIG develops AI-first products (apps and services that use machine learning to

inform and assist their users) for both insurance and investment arms of AIG. This is mainly

achieved through:

1. Incubation of disruptive innovation (via scientists, engineers and designers working

together)

2. Machine learning R&D (and publication in top AI/ML conferences and journals)

3. Provision of machine-learning advisory / consulting to AIG's global businesses.

The resulting solutions and innovations aim to reinvent AIG’s internal processes, improve AIG’s

offerings to its clients (in line with the company’s mission to be its clients’ most valued insurer),

and ultimately disrupt the wider insurance industry.

As a critical role in Science’s success, we are looking to hire NLP scientists - with Deep Learning

expertise – to join our team and contribute to the development and implementation of the

algorithmic core of a series of exciting new products / projects.

This is an exciting opportunity for those who want to enjoy state-of-the-art R&D and be

challenged and grow as a Deep Learning Scientist; along the way this role will contribute to

game-changing products for the multi-trillion-dollar insurance industry and use AIG’s (the

world’s largest insurer by many metrics) global network to deliver impact and change.

Responsibilities and Performance Objectives

􀁸 Employ the best of NLP (and Deep Learning) research for solving business problems -

disrupting the current practice in insurance and investment.

􀁸 Build and refine algorithms that can find “useful” patterns in large multi-modal data

(particularly, text, conversations, and transactional data).

􀁸 Provide the business with new product ideas, as well as data-driven apps, insights and

strategies.

􀁸 Communicate (both oral and written) with colleagues and stakeholders (both internal

and external).

􀁸 For more senior candidates: Lead, inspire and mentor junior scientists and research

assistants / interns.

Required Attributes

Both senior candidates (e.g., with years of post-doctoral and/or commercial experience) and

junior candidates are welcome to apply; we have and will offer positions appropriate to

expertise and level of experience.

The minimum required skills include:

􀁸 An advanced degree in a numeric discipline (e.g., Statistics, Machine Learning, Computer

Science, Engineering, and Physics).

􀁸 Completion of one significant project (equivalent of a PhD research project, and/or a

viable commercial product) in one or more of the hiring themes.

􀁸 Experience in core NLP and text analytics tasks and application areas (e.g., text

classification, topic detection, information extraction, Named Entity recognition, entity

resolution, Question-Answering, dialog systems, chatbots, sentiment analysis, event

detection, language modelling).

􀁸 Scientific expertise, strong track record, and real-world experience in Deep Learning,

especially with hands-on experience in hyper-parameter tuning and deep construction /

distribution (e.g., architecture design in CNN/RNN/LSTM, attention mechanisms,

parameter initialization, activation, normalization, and optimization).

􀁸 Expertise in programming (e.g., Python, C++ or Java/Scala) and computing technologies

(high-performance computing, e.g., CUDA).

􀁸 Ability to use existing deep / machine learning libraries (e.g., TensorFlow, Torch,

Theano, Caffe, scikit-learn, Deeplearning4j, and Chainer).

􀁸 Familiarity with existing Open Source NLP libraries and utilities (e.g., Stanford CoreNLP,

spaCy, fastText, AllenNLP, PyTorch-NLP, Gensim, word2vec, GloVe). Experience in

mining large-scale, multi-domain text corpora and streams.

􀁸 Experience with the data and platform aspects of the projects.

􀁸 Review, direct, guide, inspire the research of the more junior scientists in the team

(especially applicable to more senior candidates).

An ideal candidate (is not required to, but) will also have

􀁸 Track record in integrating deep learning with real-time computing (including mobile

apps and front-end systems).

􀁸 Experience in employing deep learning in a commercial setting – in collaboration with

product development teams.

􀁸 Experience in one or more the following: knowledge extraction and knowledge graphs;

text summarisation; semantic role labelling; information retrieval; Natural Language

Generation; NLP resource development and management, crowdsourcing; NLP

evaluation and testing.

􀁸 Publication record in (and willingness to represent AIG in) top machine learning and NLP

journals (e.g., CL, NLE, AI, TPAMI, IJCV, and JMLR) and conferences (e.g., NIPS, ICML,

ACL, EMNLP, NAACL, EACL, COLING, LREC, CoNLL, IJCNLP, ICLR, and IJCV).

􀁸 Experience of working with engineering and design / product teams.

􀁸 Broad knowledge of machine learning (including topics such as graph theory,

hierarchical modeling, and Bayesian inference).

􀁸 Practical experience of modern big-data computing ecosystems (e.g., Apache Spark).

􀁸 The ability to engage with business stakeholders (with excellent oral and written

communication skills).

At AIG, we believe that diversity and inclusion are critical to our future and our mission – creating a foundation for a creative workplace that leads to innovation, growth, and profitability. Through a wide variety of programs and initiatives, we invest in each employee, seeking to ensure that our people are not only respected as individuals, but also truly valued for their unique perspectives.